Federato vs Kalepa (2026)
The sharpest question in this pairing is whether the machine ever decides. Federato says yes and describes exactly when. The system drafts the best triaged deals and explains its reasoning while the underwriter adjusts and owns the relationship. A defined subset of deals that clearly fit strategy is fully executed by the AI under predefined rules and guardrails, and underwriters randomly audit those automated decisions for quality control. Federato names in public which decisions are automated, what constrains them and what the sampling control is, where most vendors leave all three implicit. Kalepa says no and draws the line just as narrowly: Copilot reads, cross references and scores, the underwriter decides, with the reasoning behind every flag visible. Both document oversight in detail from opposite directions, which is the point. The second split is architectural and equally deliberate. Federato sits on top of the policy administration core with native connections to the three dominant platforms in this market, replacing the underwriter desktop rather than the system of record, on an eight to twelve week implementation. Kalepa works on day one with no integrations at all, by design.
- You want a defined subset of deals to execute without an underwriter. Clearly on strategy deals are fully executed by the system under predefined rules and guardrails, with random underwriter audit sampling behind them, and the whole design is published.
- The platform has to live with your core. Native interface integrations reach the three dominant policy administration platforms, so the product replaces the underwriter desktop while leaving the system of record in place, covering submission intake, pricing, quoting, documentation and referral workflow through to invoice.
- Portfolio position drives your selection. Real time exposure and accumulation analysis sits alongside submission triage against stated appetite, so a risk is judged against the book rather than on its own merits.
- Your reinsurers are part of the argument. The platform produces audit ready evidence of appetite adherence and referral activity that carriers present to reinsurers, which puts a capital provider with its own money at risk in the position of scrutinizing the discipline the system enforces.
- You want the underwriter to remain the decision maker. The division of labor is stated narrowly and holds throughout: Copilot reads, cross references and scores, the underwriter decides, and no subset of deals is executed automatically.
- You cannot wait a quarter to see value. Copilot works out of the box on day one with no integrations, with inbox, portal and policy system connections available afterwards, against an eight to twelve week implementation at the alternative.
- The submission is only half the risk. Copilot cross references billions of data points from loss runs, web sources and third party providers to surface exposures the submission did not mention, rather than triaging what the broker chose to disclose.
- You want AI to be the whole product. Kalepa had no existence before the models. Strip the models from Federato and a unified underwriting workflow with portfolio visibility remains, which is a substantial product in its own right.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Federato and Kalepa are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Federato | Kalepa | |
|---|---|---|
| Primary category | Insurance AI | Insurance AI |
| Founded | Not published | 2018 |
| Headquarters | Not published | New York, New York, United States |
| Website | www.federato.ai | www.kalepa.com |
Side by Side
Select any grade to read the note behind it.
| Axis | F Federato |
K Kalepa |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
Product updates
The latest logged changes from each vendor, newest first, each linked to its source.
Federato
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Product / capability
Federato launched Federato Claims, a claims management module built on its existing policy lifecycle platform.
Source
The short version of each
Federato
Federato sells an underwriting platform for property and casualty carriers, managing general agents and mutuals. It replaces the underwriter desktop while sitting on top of the existing policy administration core. It triages submissions against stated appetite, surfaces real time portfolio exposure and accumulation, and drafts quotes with reasoning shown. A defined subset of clearly on strategy deals is executed automatically under preset guardrails, with underwriter audit sampling behind it. According to the AI FinTech Index, its published autonomy design names which decisions are automated, what constrains them and what the sampling control is. It documents native connections to the three dominant policy administration platforms, and its customer evidence includes named executives and falsifiable hit ratio claims. It identifies no model provider, external data source or subprocessor anywhere, and describes no testing for unfair discrimination against a state framework whose obligations land on the carrier.
Source: AI FinTech Index, 2026
Kalepa
Kalepa builds Copilot, an underwriting workbench for commercial and specialty property and casualty insurers. Copilot reads a submission on arrival and cross references billions of data points from loss runs, web and third party sources. It surfaces exposures the submission did not mention and prioritizes risks by likelihood to bind and fit against carrier appetite. Independent judging agents validate critical fields, and the division of labor is stated: Copilot scores and the underwriter decides. According to the AI FinTech Index, AI is central to the product, which had no existence before the models. Oversight and customer evidence are documented in detail, with named users spanning Munich Re Specialty, Bowhead and SECURA. Integration depth is traded away by design, with no policy administration platform named. A market leading accuracy claim carries no figure, and the named carriers and quantified results never attach to the same account. Crime and legal action data are used as underwriting exposure with no fairness testing published.
Source: AI FinTech Index, 2026
Common questions
Is Federato better than Kalepa for property and casualty underwriting?
They answer different questions about who decides. Federato executes a defined subset of clearly on strategy deals automatically under guardrails with random underwriter audit, and sits on top of the policy administration core with native connections to the three dominant platforms. Kalepa never automates the decision, stating that Copilot scores and the underwriter decides, and works on day one with no integrations. Both document oversight and customer results in detail. Federato documents deep integrations where Kalepa forgoes them by design, and AI is central to Kalepa where it is one layer of Federato's broader platform. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
Which one actually automates underwriting decisions?
Federato, within a stated boundary. It names three distinct modes rather than gesturing at human in the loop: the system drafts the best triaged deals and shows its reasoning while the underwriter adjusts, a defined subset that clearly fits strategy is fully executed under predefined rules and guardrails, and those automated decisions are randomly audited by underwriters for quality control. Kalepa automates no decision. Its ensemble engine decides where to automate rather than assist within the analysis, but the bind decision stays with the underwriter. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
How central is AI to each product?
Strip the models from Kalepa and the screen is empty, since the structured read of a messy submission, the cross reference against external data and the priority score against appetite are all model output, and no system of record sits beneath supplying anything. Strip them from Federato and a unified underwriting workflow with portfolio visibility survives. The platform's own account of its value supports that: it describes itself as consolidating analytics, scoring and workflow that underwriters previously stitched together from several vendors. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
How do their implementations compare?
Federato states eight to twelve weeks and reaches native integrations with the three dominant policy administration platforms, spanning submission intake through pricing, quoting, documentation and referral workflow to invoice, with a partner portal for brokers. Kalepa states day one with no integrations required, adding inbox, portal and policy system connections afterwards. That is a real commercial advantage against implementations measured in quarters, and it comes at the cost of integration depth, since no policy administration system, rating engine or integration partner is named at all. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
What evidence does each publish?
Both publish strong customer evidence. Federato names customers at the top of the market with executives quoted by name, including the chief underwriting officer of a global carrier's North American arm describing the platform as the unified core system for the entire policy lifecycle. It publishes falsifiable claims of hit ratio improvements of nine to twenty four percent, alongside original research surveying 500 underwriters and executives. Kalepa names Munich Re Specialty, Bowhead and SECURA, and publishes a case study on an unnamed mid market division of a top 15 global carrier reporting time to quote down 40 percent, bind rate from 30 to 34.5 percent and appetite compliance above 95 percent with 15 percent fewer underwriters, so its names and its numbers do not attach to the same account. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
Where does each publish the most, and the least?
Federato publishes most on what it automates and who checks it, its integrations and its customer results. It publishes nothing on the models and providers it is built on, and says little about bias and fairness. Kalepa publishes most on oversight and customer results, and AI is central to the product. It says little about integrations, which it forgoes by design, about how customer data is kept apart and used, or about bias and fairness. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 5, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Insurance AI page.
Federato publishes nothing on the models and providers it is built on. No public material identifies the model providers behind triage, scoring and agentic quoting, the external data sources feeding exposure and accumulation analysis, or its subprocessors. The gap is specific to this category. Carriers price risk on data, so which third party exposure, catastrophe or property datasets inform a recommendation is a question underwriters would ordinarily ask before trusting a number.
Kalepa says only a little more, because a United States server commitment establishes country of processing. Its ensemble engine selects models it never names. Both say little about bias and fairness, against a framework that has moved faster in insurance than in other parts of financial services. State regulators have adopted a model bulletin on insurer use of AI, and several states impose testing duties around unfair discrimination.
The obligation lands on the carrier, which makes vendor documentation a practical necessity for the buyer rather than a courtesy. Neither describes testing for unfair discrimination in triage or pricing, and neither explains what evidence a carrier could hand a regulator asking how automated decisions were validated. Kalepa's exposure is more concrete.
Copilot surfaces criminal activity and pending legal actions from open web sources as underwriting exposures, and its own case study names violent incidents at insured premises, importing whatever geographic patterns that data carries. Neither states whether one carrier's submissions, pricing or loss experience inform models serving a competitor, and risk selection is precisely where carriers compete.